Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Engineer

Elecnor Deimos
Harwell
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer – Insurance

Machine Learning Engineer (SC Cleared)

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

DEIMOS is looking for an engineer to join the Computer Vision/Artificial Intelligence (CV/AI) Competence Centre of the Avionics Business Unit, Flight Systems Directorate.

This role focuses on supporting Deimos’ AI/CV flight systems team in researching, developing, deploying and scaling our computer vision portfolio for onboard processing applications in Space. You will work on Machine Learning projects and products throughout their lifecycle – from early-phase R&D activities to productization and deployment.

The work of the AI/CV Competence Centre is oriented to the design, development, specification, and validation of Computer Vision solutions for embedded flight segment applications, including real-time advanced onboard data processing and intelligent decision making.

This preferred locations for this role are either Harwell, UK, or Madrid, Spain, although other Deimos sites may also be considered.

Duties:

The main responsibilities are:

Research, design, implement, and deploy machine learning models and algorithms that address specific challenges and opportunities related to on-board processing in Space. Collaborate with team-members and clients across Europe to understand project requirements, objectives, and constraints. Process and analyse datasets to extract meaningful insights and features for model development. Design, implement and maintain industry-standard MLOps infrastructure for new and existing ML products Optimize and standardize ML training and validation processes, data warehousing and pipelines.

Education:

Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field.

Professional Experience:

The position will be tailored to the level of experience; practical industry experience deploying and maintaining ML systems in production would be viewed very positively.

Technical Requirements:

Required:

Strong foundation in machine learning algorithms, statistics, and data structures within relevant technical projects. Proficiency in programming languages, frameworks, and tools, such as Python, TensorFlow, PyTorch. Experience with data preprocessing, feature engineering, and model evaluation techniques.

Highly Desirable:

Experience working on aerospace-related projects Experience deploying MLOps solutions and working within CI/CD frameworks Experience with Linux systems and cloud infrastructure (AWS, Azure, etc.) Experience developing embedded ML applications (C++, CUDA, TensorRT)

Language Skills:

Good level of English, spoken and written

Personal Skills:

Capability to integrate in and work within a trans-European team Solid organisational, analytical and reporting skills Autonomy and willingness to take initiative Excellent communication skills Energetic, positive team player mentality

Ref.:

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

The Best Free Tools & Platforms to Practise Machine Learning Skills in 2025/26

Machine learning (ML) has become one of the most in-demand career paths in technology. From predicting customer behaviour in retail to detecting fraud in banking and enabling medical breakthroughs in healthcare, ML is transforming industries across the UK and beyond. But here’s the truth: employers don’t just want candidates who have read about machine learning in textbooks. They want evidence that you can actually build, train, and deploy models. That means practising with real tools, working with real datasets, and solving real problems. The good news is that you don’t need to pay for expensive software or courses to get started. A wide range of free, open-source tools and platforms allow you to learn machine learning skills hands-on. Whether you’re a beginner or preparing for advanced roles, you can practise everything from simple linear regression to deploying deep learning models — at no cost. In this guide, we’ll explore the best free tools and platforms to practise machine learning skills in 2025, and how to use them effectively to build a portfolio that UK employers will notice.

Top 10 Skills in Machine Learning According to LinkedIn & Indeed Job Postings

Machine learning (ML) is at the forefront of innovation, powering systems in finance, healthcare, retail, logistics, and beyond in the UK. As organisations leverage ML for predictive analytics, automation, and intelligent systems, demand for skilled practitioners continues to grow. So, which skills are most in demand? Drawing on insights from LinkedIn and Indeed, this article outlines the Top 10 machine learning skills UK employers are looking for in 2025. You'll learn how to demonstrate these capabilities through your CV, interviews, and real-world projects.

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has become one of the most powerful forces reshaping the modern world. From voice assistants and recommendation engines to fraud detection and medical imaging, it underpins countless applications. ML is no longer confined to research labs—it powers business models, public services, and consumer technologies across the globe. In the UK, demand for machine learning professionals has risen dramatically. Organisations in finance, retail, healthcare, and defence are embedding ML into their operations. Start-ups in Cambridge, London, and Edinburgh are pioneering innovations, while government-backed initiatives aim to position the UK as a global AI leader. Salaries for ML engineers and researchers are among the highest in the tech sector. Yet despite its current importance, machine learning is only at the beginning of its journey. Advances in generative AI, quantum computing, robotics, and ethical governance will reshape the profession. Many of the most vital machine learning jobs of the next two decades don’t exist today. This article explores why new careers will emerge, the roles likely to appear, how today’s roles will evolve, why the UK is well positioned, and how professionals can prepare now.